机器人相关学术速递[12.16]

cs.RO机器人相关，共计11篇

【1】 Estimating Uncertainty For Vehicle Motion Prediction on Yandex Shifts Dataset 标题：基于Yandex位移数据集的车辆运动预测不确定性估计 链接：https://arxiv.org/abs/2112.08355

作者：Alexey Pustynnikov,Dmitry Eremeev 备注：Bayesian Deep Learning Workshop, NeurIPS 2021 摘要：环境智能体的运动预测是自主驾驶中的一项重要任务，因为它与驾驶员的安全密切相关。车辆运动预测（VMP）换档轨迹挑战的重点是开发对分布换档具有鲁棒性且能够测量其预测不确定性的模型。在这项工作中，我们提出了一种方法，该方法显著改进了基准测试，并在排行榜上排名第二。 摘要：Motion prediction of surrounding agents is an important task in context of autonomous driving since it is closely related to driver's safety. Vehicle Motion Prediction (VMP) track of Shifts Challenge focuses on developing models which are robust to distributional shift and able to measure uncertainty of their predictions. In this work we present the approach that significantly improved provided benchmark and took 2nd place on the leaderboard.

【2】 ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery 标题：ST-MTL：机器人手术器械跟踪时预测扫描路径的时空多任务学习模型 链接：https://arxiv.org/abs/2112.08189

作者：Mobarakol Islam,Vibashan VS,Chwee Ming Lim,Hongliang Ren 备注：12 pages 摘要：在图像引导的机器人手术中，跟踪仪器在任务导向注意力表征学习中具有巨大的潜力。结合认知能力，使摄像机控制自动化，使外科医生能够更加专注于处理手术器械。目的是缩短手术时间，方便外科医生和患者进行手术。我们提出了一种端到端可训练的时空多任务学习（ST-MTL）模型，该模型具有共享编码器和时空解码器，用于实时手术器械分割和面向任务的显著性检测。在共享参数的MTL模型中，将多个损失函数优化为一个收敛点仍然是一个开放的挑战。我们采用一种新的异步时空优化（ASTO）技术，通过计算每个解码器的独立梯度来解决这个问题。我们还设计了一个竞争性的挤压和激励单元，通过铸造一个保持弱特征、激励强特征并执行动态空间和通道特征重新校准的跳过连接。为了捕获更好的长期时空依赖性，我们通过连接连续帧的高级编码器特征来增强长短时记忆（LSTM）模块。我们还引入了Sinkhorn正则化损失，通过保持计算效率来增强面向任务的显著性检测。我们在MICCAI 2017机器人仪器分割挑战的数据集上生成任务感知显著性图和仪器的扫描路径。与最先进的分割和显著性方法相比，我们的模型优于大多数评估指标，并在挑战中产生了出色的性能。 摘要：Representation learning of the task-oriented attention while tracking instrument holds vast potential in image-guided robotic surgery. Incorporating cognitive ability to automate the camera control enables the surgeon to concentrate more on dealing with surgical instruments. The objective is to reduce the operation time and facilitate the surgery for both surgeons and patients. We propose an end-to-end trainable Spatio-Temporal Multi-Task Learning (ST-MTL) model with a shared encoder and spatio-temporal decoders for the real-time surgical instrument segmentation and task-oriented saliency detection. In the MTL model of shared parameters, optimizing multiple loss functions into a convergence point is still an open challenge. We tackle the problem with a novel asynchronous spatio-temporal optimization (ASTO) technique by calculating independent gradients for each decoder. We also design a competitive squeeze and excitation unit by casting a skip connection that retains weak features, excites strong features, and performs dynamic spatial and channel-wise feature recalibration. To capture better long term spatio-temporal dependencies, we enhance the long-short term memory (LSTM) module by concatenating high-level encoder features of consecutive frames. We also introduce Sinkhorn regularized loss to enhance task-oriented saliency detection by preserving computational efficiency. We generate the task-aware saliency maps and scanpath of the instruments on the dataset of the MICCAI 2017 robotic instrument segmentation challenge. Compared to the state-of-the-art segmentation and saliency methods, our model outperforms most of the evaluation metrics and produces an outstanding performance in the challenge.

【3】 Enhance Connectivity of Promising Regions for Sampling-based Path Planning 标题：基于采样的路径规划中增强前景区域连通性的研究 链接：https://arxiv.org/abs/2112.08106

作者：Han Ma,Chenming Li,Jianbang Liu,Jiankun Wang,Max Q. -H. Meng 摘要：基于采样的路径规划算法通常采用均匀采样的方法来搜索状态空间。然而，在许多情况下，统一采样可能会导致不必要的探索，例如有几个死胡同的环境。我们以前的工作建议使用有希望的区域来指导取样过程，以解决这个问题。然而，预测的有希望的区域通常是断开的，这意味着它们无法连接开始和目标状态，从而导致缺乏概率完整性。这项工作的重点是增强预测的有希望区域的连通性。我们提出的方法回归了边缘在x和y方向上的连通概率。此外，它还计算有希望的边缘在损失中的权重，以引导神经网络更加关注有希望区域的连通性。我们进行了一系列模拟实验，结果表明，前景区域的连通性得到了显著改善。此外，我们还分析了连通性对基于采样的路径规划算法的影响，得出连通性在保持算法性能方面起着至关重要的作用的结论。 摘要：Sampling-based path planning algorithms usually implement uniform sampling methods to search the state space. However, uniform sampling may lead to unnecessary exploration in many scenarios, such as the environment with a few dead ends. Our previous work proposes to use the promising region to guide the sampling process to address the issue. However, the predicted promising regions are often disconnected, which means they cannot connect the start and goal state, resulting in a lack of probabilistic completeness. This work focuses on enhancing the connectivity of predicted promising regions. Our proposed method regresses the connectivity probability of the edges in the x and y directions. In addition, it calculates the weight of the promising edges in loss to guide the neural network to pay more attention to the connectivity of the promising regions. We conduct a series of simulation experiments, and the results show that the connectivity of promising regions improves significantly. Furthermore, we analyze the effect of connectivity on sampling-based path planning algorithms and conclude that connectivity plays an essential role in maintaining algorithm performance.

【4】 Visually Guided UGV for Autonomous Mobile Manipulation in Dynamic and Unstructured GPS Denied Environments 标题：用于动态和非结构化GPS拒绝环境中自主移动操作的视觉导引UGV 链接：https://arxiv.org/abs/2112.08024

作者：Mohit Vohra,Laxmidhar Behera 备注：Paper has been accepted for publication in International Conference On Computational Intelligence - ICCI 2021 摘要：提出了一种无人地面车辆（UGV）在自主模式下执行高度复杂的目标操纵任务的机器人解决方案。本文主要致力于开发一个能够在GPS环境中组装基本块以构建大型3D结构的自主机器人系统。本系统论文的主要贡献是i）基于深度学习的统一多任务视觉感知系统的设计，用于目标检测、零件检测、实例分割和跟踪，ii）用于鲁棒抓取的电磁抓取器设计，和iii）系统集成，其中集成多个系统组件以开发优化的软件堆栈。本工作详细介绍了用于上述应用的UGV的整个机电和算法设计。通过几次严格的实验，报告了整个系统的性能和功效。 摘要：A robotic solution for the unmanned ground vehicles (UGVs) to execute the highly complex task of object manipulation in an autonomous mode is presented. This paper primarily focuses on developing an autonomous robotic system capable of assembling elementary blocks to build the large 3D structures in GPS-denied environments. The key contributions of this system paper are i) Designing of a deep learning-based unified multi-task visual perception system for object detection, part-detection, instance segmentation, and tracking, ii) an electromagnetic gripper design for robust grasping, and iii) system integration in which multiple system components are integrated to develop an optimized software stack. The entire mechatronic and algorithmic design of UGV for the above application is detailed in this work. The performance and efficacy of the overall system are reported through several rigorous experiments.

【5】 Learning Submodular Objectives for Team Environmental Monitoring 标题：学习团队环境监测的子模块目标 链接：https://arxiv.org/abs/2112.08000

作者：Nils Wilde,Armin Sadeghi,Stephen L. Smith 摘要：在本文中，我们研究了著名的团队定向问题，其中一队机器人通过访问地点收集奖励。通常，奖励被假定为机器人已知；然而，在环境监测或场景重建等应用中，回报往往是主观的，具体规定这些回报很有挑战性。我们提出了一个框架，通过向用户提供替代解决方案来了解用户的未知偏好，并且用户对所提出的替代解决方案进行排名。我们考虑用户的两种情况：1）一个确定性用户，它提供了对替代方案的最优排序，和2）根据未知的概率分布提供最优排序的有噪声的用户。对于确定性用户，我们提出了一个最小化与最优解最大偏差的界的框架，即遗憾。我们采用这种方法来捕获有噪声的用户，并最小化预期的遗憾。最后，我们使用真实世界数据集对环境监测问题进行了大量实验，证明了学习用户偏好的重要性以及所提出方法的性能。 摘要：In this paper, we study the well-known team orienteering problem where a fleet of robots collects rewards by visiting locations. Usually, the rewards are assumed to be known to the robots; however, in applications such as environmental monitoring or scene reconstruction, the rewards are often subjective and specifying them is challenging. We propose a framework to learn the unknown preferences of the user by presenting alternative solutions to them, and the user provides a ranking on the proposed alternative solutions. We consider the two cases for the user: 1) a deterministic user which provides the optimal ranking for the alternative solutions, and 2) a noisy user which provides the optimal ranking according to an unknown probability distribution. For the deterministic user we propose a framework to minimize a bound on the maximum deviation from the optimal solution, namely regret. We adapt the approach to capture the noisy user and minimize the expected regret. Finally, we demonstrate the importance of learning user preferences and the performance of the proposed methods in an extensive set of experimental results using real world datasets for environmental monitoring problems.

【6】 Homography Decomposition Networks for Planar Object Tracking 标题：单应分解网络在平面目标跟踪中的应用 链接：https://arxiv.org/abs/2112.07909

作者：Xinrui Zhan,Yueran Liu,Jianke Zhu,Yang Li 备注：Accepted at AAAI 2022, preprint version 摘要：平面目标跟踪在人工智能应用中起着重要作用，如机器人技术、视觉伺服和视觉SLAM。尽管以前的平面跟踪器在大多数情况下都能很好地工作，但由于两个连续帧之间的快速运动和大变换，它仍然是一项具有挑战性的任务。这个问题背后的根本原因是当单应参数空间的搜索范围变大时，这样一个非线性系统的条件数不稳定地变化。为此，我们提出了一种新的单应分解网络（HDN）方法，通过将单应变换分解为两组，大大减少并稳定了条件数。具体地说，设计了一个相似变换估计器，通过深度卷积等变网络对第一组进行稳健预测。利用高置信度的尺度和旋转估计，通过简单的回归模型估计残差变换。此外，所提出的端到端网络以半监督方式进行训练。大量实验表明，在具有挑战性的POT、UCSB和POIC数据集上，我们提出的方法在很大程度上优于最新的平面跟踪方法。 摘要：Planar object tracking plays an important role in AI applications, such as robotics, visual servoing, and visual SLAM. Although the previous planar trackers work well in most scenarios, it is still a challenging task due to the rapid motion and large transformation between two consecutive frames. The essential reason behind this problem is that the condition number of such a non-linear system changes unstably when the searching range of the homography parameter space becomes larger. To this end, we propose a novel Homography Decomposition Networks~(HDN) approach that drastically reduces and stabilizes the condition number by decomposing the homography transformation into two groups. Specifically, a similarity transformation estimator is designed to predict the first group robustly by a deep convolution equivariant network. By taking advantage of the scale and rotation estimation with high confidence, a residual transformation is estimated by a simple regression model. Furthermore, the proposed end-to-end network is trained in a semi-supervised fashion. Extensive experiments show that our proposed approach outperforms the state-of-the-art planar tracking methods at a large margin on the challenging POT, UCSB and POIC datasets.

【7】 Environmental force sensing enables robots to traverse cluttered obstacles with interaction 标题：环境力感知使机器人能够通过交互穿越杂乱的障碍物 链接：https://arxiv.org/abs/2112.07900

作者：Qihan Xuan,Yaqing Wang,Chen Li 摘要：许多应用需要机器人在有大型障碍物的地形中移动，例如自动驾驶、搜索和救援以及地外探测。尽管机器人已经擅长避开稀疏的障碍物，但它们仍然难以穿越杂乱的障碍物。受蟑螂的启发，蟑螂以各种方式使用和响应与障碍物的物理交互，以穿过具有不同刚度的草状梁，在这里，我们开发了一个微型机器人的物理模型，该机器人能够感知向前推进的环境力，以穿过两根梁，模拟和理解凌乱障碍物的穿越。梁的刚度和挠度位置等特性可以通过测量的噪声梁接触力进行估计，其保真度随着传感时间的增加而增加。利用这些估计值，该模型预测了使用势能屏障定义的穿越成本，并将其用于规划和控制机器人以最小成本生成和跟踪要穿越的轨迹。当遇到刚性梁时，模拟机器人从一个更昂贵的俯仰模式过渡到一个成本更低的横摇模式进行移动。当遇到脆弱的横梁时，它选择推动横梁，而不是避开横梁。最后，我们开发了一个物理机器人，并证明了该估计方法的有效性。 摘要：Many applications require robots to move through terrain with large obstacles, such as self-driving, search and rescue, and extraterrestrial exploration. Although robots are already excellent at avoiding sparse obstacles, they still struggle in traversing cluttered obstacles. Inspired by cockroaches that use and respond to physical interaction with obstacles in various ways to traverse grass-like beams with different stiffness, here we developed a physics model of a minimalistic robot capable of environmental force sensing propelled forward to traverse two beams to simulate and understand the traversal of cluttered obstacles. Beam properties like stiffness and deflection locations could be estimated from the noisy beam contact forces measured, whose fidelity increased with sensing time. Using these estimates, the model predicted the cost of traversal defined using potential energy barriers and used it to plan and control the robot to generate and track a trajectory to traverse with minimal cost. When encountering stiff beams, the simulation robot transitioned from a more costly pitch mode to a less costly roll mode to traverse. When encountering flimsy beams, it chose to push cross beams with less energy cost than avoiding beams. Finally, we developed a physical robot and demonstrated the usefulness of the estimation method.

【8】 A Comparison of Robust Kalman Filters for Improving Wheel-Inertial Odometry in Planetary Rovers 标题：改进行星漫游车轮惯性里程计的鲁棒卡尔曼滤波方法的比较 链接：https://arxiv.org/abs/2112.07872

作者：Shounak Das,Cagri Kilic,Ryan Watson,Jason Gross 摘要：本文比较了自适应和鲁棒卡尔曼滤波算法在改善低特征崎岖地形车轮惯性里程测量中的性能。这些方法包括经典的自适应和鲁棒方法以及变分方法，这些方法在类似于行星探测中遇到的地形的轮式探测车上进行了实验评估。与经典自适应滤波器相比，变分滤波器显示出更高的求解精度，并且能够处理错误的车轮里程计测量，并在较长距离内保持良好的定位，而不会出现明显的漂移。我们还展示了参数的变化如何影响本地化性能。 摘要：This paper compares the performance of adaptive and robust Kalman filter algorithms in improving wheel-inertial odometry on low featured rough terrain. Approaches include classical adaptive and robust methods as well as variational methods, which are evaluated experimentally on a wheeled rover in terrain similar to what would be encountered in planetary exploration. Variational filters show improved solution accuracy compared to the classical adaptive filters and are able to handle erroneous wheel odometry measurements and keep good localization for longer distances without significant drift. We also show how varying the parameters affects localization performance.

【9】 Review of Factor Graphs for Robust GNSS Applications 标题：强健GNSS应用的因子图研究综述 链接：https://arxiv.org/abs/2112.07794

作者：Shounak Das,Ryan Watson,Jason Gross 摘要：因子图最近已成为全球导航卫星系统定位的另一种解决方法。在这篇文章中，我们回顾了因子图是如何在GNSS中实现的，它们相对于卡尔曼滤波器的一些优势，以及它们在使定位解决方案对降级测量更具鲁棒性方面的重要性。我们还将讨论因子图如何成为野外无线电导航社区的重要工具。 摘要：Factor graphs have recently emerged as an alternative solution method for GNSS positioning. In this article, we review how factor graphs are implemented in GNSS, some of their advantages over Kalman Filters, and their importance in making positioning solutions more robust to degraded measurements. We also talk about how factor graphs can be an important tool for the field radio-navigation community.

【10】 Revisiting 3D Object Detection From an Egocentric Perspective 标题：从自我中心的视角重新审视三维目标检测 链接：https://arxiv.org/abs/2112.07787

作者：Boyang Deng,Charles R. Qi,Mahyar Najibi,Thomas Funkhouser,Yin Zhou,Dragomir Anguelov 备注：Published in NeurIPS 2021 摘要：三维目标检测是自动驾驶等安全关键机器人应用的关键模块。对于这些应用程序，我们最关心的是检测如何影响自我代理的行为和安全（以自我为中心的观点）。直观地说，当物体的几何结构更容易干扰自我主体的运动轨迹时，我们会寻求更精确的描述。然而，当前的检测指标基于联合上的盒交叉（IoU），是以对象为中心的，不能捕获对象和自我代理之间的时空关系。为了解决这个问题，我们提出了一种新的以自我为中心的三维目标检测方法，即支持距离误差（SDE）。基于SDE的分析表明，以自我为中心的检测质量受包围盒的粗糙几何结构的限制。鉴于SDE将受益于更精确的几何描述，我们建议将对象表示为amodal轮廓，特别是amodal星形多边形，并设计一个简单的模型StarPoly来预测此类轮廓。我们在大规模Waymo开放数据集上的实验表明，与IoU相比，SDE更好地反映了检测质量对ego代理安全性的影响；与最近的3D目标检测器相比，StarPoly估计的轮廓持续改善了以自我为中心的检测质量。 摘要：3D object detection is a key module for safety-critical robotics applications such as autonomous driving. For these applications, we care most about how the detections affect the ego-agent's behavior and safety (the egocentric perspective). Intuitively, we seek more accurate descriptions of object geometry when it's more likely to interfere with the ego-agent's motion trajectory. However, current detection metrics, based on box Intersection-over-Union (IoU), are object-centric and aren't designed to capture the spatio-temporal relationship between objects and the ego-agent. To address this issue, we propose a new egocentric measure to evaluate 3D object detection, namely Support Distance Error (SDE). Our analysis based on SDE reveals that the egocentric detection quality is bounded by the coarse geometry of the bounding boxes. Given the insight that SDE would benefit from more accurate geometry descriptions, we propose to represent objects as amodal contours, specifically amodal star-shaped polygons, and devise a simple model, StarPoly, to predict such contours. Our experiments on the large-scale Waymo Open Dataset show that SDE better reflects the impact of detection quality on the ego-agent's safety compared to IoU; and the estimated contours from StarPoly consistently improve the egocentric detection quality over recent 3D object detectors.

【11】 Autonomous Navigation System from Simultaneous Localization and Mapping 标题：基于同步定位和测绘的自主导航系统 链接：https://arxiv.org/abs/2112.07723

作者：Micheal Caracciolo,Owen Casciotti,Christopher Lloyd,Ernesto Sola-Thomas,Matthew Weaver,Kyle Bielby,Md Abdul Baset Sarker,Masudul H. Imtiaz 摘要：本文介绍了一种基于同步定位和地图（SLAM）的自主导航系统的开发。这项研究的动机是找到一种自主导航室内空间的解决方案。内部导航具有挑战性，因为它可能会不断发展。解决这一问题对于清洁、卫生行业和制造业等众多服务业来说都是必要的。本文的重点是描述为该自治系统开发的基于SLAM的软件体系结构。评估了该系统面向智能轮椅的潜在应用。当前的内部导航解决方案需要某种引导线，比如地板上的黑线。有了这个建议的解决方案，内部不需要改造来适应这个解决方案。此应用程序的源代码已开放源代码，因此可以将其重新用于类似的应用程序。此外，这个开源项目预计将由广泛的开源社区在过去的状态下进行改进。 摘要：This paper presents the development of a Simultaneous Localization and Mapping (SLAM) based Autonomous Navigation system. The motivation for this study was to find a solution for navigating interior spaces autonomously. Interior navigation is challenging as it can be forever evolving. Solving this issue is necessary for multitude of services, like cleaning, the health industry, and in manufacturing industries. The focus of this paper is the description of the SLAM-based software architecture developed for this proposed autonomous system. A potential application of this system, oriented to a smart wheelchair, was evaluated. Current interior navigation solutions require some sort of guiding line, like a black line on the floor. With this proposed solution, interiors do not require renovation to accommodate this solution. The source code of this application has been made open source so that it could be re-purposed for a similar application. Also, this open-source project is envisioned to be improved by the broad open-source community upon past its current state.